Differential expression analysis of LCM RNA Data

Managing Packages Using Renv

To run this code in my project using the renv environment, run the following lines of code

install.packages("renv") #install the package on the new computer (may not be necessary if renv bootstraps itself as expected)
renv::restore() #reinstall all the package versions in the renv lockfile

Load packages

require("genefilter")
## Loading required package: genefilter
require("DESeq2")
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require("ashr")
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require("ggplot2")
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require("vsn")
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require("RColorBrewer")
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sessionInfo() #provides list of loaded packages and version of R.
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
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## [70] mvtnorm_1.3-2           XML_3.99-0.17           grid_4.3.2             
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save_ggplot <- function(plot, filename, width = 10, height = 7, units = "in", dpi = 300) {
  # Display plot
  print(plot)
  
  # Save plot
  ggsave(filename = paste0(filename, ".png"), plot = plot, width = width, height = height, units = units, dpi = dpi)
}

Read and clean count matrix and metadata

Read in raw count data

counts_raw <- read.csv("../output_RNA/stringtie-GeneExt/LCM_RNA_gene_count_matrix.csv", row.names = 1) #load in data

gene_id,LCM_15,LCM_16,LCM_20,LCM_21,LCM_26,LCM_27,LCM_4,LCM_5,LCM_8,LCM_9

Read in metadata

meta <- read.csv("../data_RNA/LCM_RNA_metadata.csv") %>%
            dplyr::arrange(Sample) %>%
            mutate(across(c(Tissue, Fragment, Section_Date, LCM_Date), factor)) # Set variables as factors 

meta$Tissue <- factor(meta$Tissue, levels = c("OralEpi","Aboral")) #we want OralEpi to be the baseline

Data sanity checks!

all(meta$Sample %in% colnames(counts_raw)) #are all of the sample names in the metadata column names in the gene count matrix? Should be TRUE
## [1] TRUE
all(meta$Sample == colnames(counts_raw)) #are they the same in the same order? Should be TRUE
## [1] TRUE

pOverA filtering to reduce dataset

ffun<-filterfun(pOverA(0.5,10))  #Keep genes expressed in at least 50% of samples -
counts_filt_poa <- genefilter((counts_raw), ffun) #apply filter

filtered_counts <- counts_raw[counts_filt_poa,] #keep only rows that passed filter

cat("Number of genes after filtering:", sum(counts_filt_poa))
## Number of genes after filtering: 14464
write.csv(filtered_counts, "../output_RNA/differential_expression/filtered_counts.csv")

There are now 14464 genes in the filtered dataset.

Data sanity checks:

all(meta$Sample %in% colnames(filtered_counts)) #are all of the sample names in the metadata column names in the gene count matrix?
## [1] TRUE
all(meta$Sample == colnames(filtered_counts))  #are they the same in the same order? Should be TRUE
## [1] TRUE

DESeq2

Create DESeq object and run DESeq2

dds <- DESeqDataSetFromMatrix(countData = filtered_counts,
                              colData = meta,
                              design= ~ Fragment + Tissue)

dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing

Extract results for Aboral vs. OralEpi contrast

res <- results(dds, contrast = c("Tissue","Aboral","OralEpi"))
resLFC <- lfcShrink(dds, coef="Tissue_Aboral_vs_OralEpi", res=res, type = "apeglm")
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
##     sequence count data: removing the noise and preserving large differences.
##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895

Extract results for adjusted p-value < 0.05

res <- resLFC

resOrdered <- res[order(res$pvalue),]# save differentially expressed genes

DE_05 <- as.data.frame(resOrdered) %>% filter(padj < 0.05)
DE_05_Up <- DE_05 %>% filter(log2FoldChange > 0) #Higher in Aboral, Lower in OralEpi
DE_05_Down <- DE_05 %>% filter(log2FoldChange < 0) #Lower in Aboral, Higher in OralEpi

nrow(DE_05)
## [1] 3606
nrow(DE_05_Up) #Higher in Aboral, Lower in OralEpi
## [1] 804
nrow(DE_05_Down) #Lower in Aboral, Higher in OralEpi
## [1] 2802
write.csv(as.data.frame(resOrdered), 
          file="../output_RNA/differential_expression/DESeq_results.csv")

write.csv(DE_05, 
          file="../output_RNA/differential_expression/DEG_05.csv")

Visualizing Differential Expression

EnhancedVolcano(resLFC, 
    lab = rownames(resLFC),
    x = 'log2FoldChange',
    y = 'pvalue')

Plots

plotMA(results(dds, contrast = c("Tissue","Aboral","OralEpi")), ylim=c(-20,20))

plotMA(resLFC, ylim=c(-20,20))

Log2 Fold Change Comparison

# because we are interested in the comparison and not the intercept, we set 'coef=2'
resNorm <- lfcShrink(dds, coef=6, type="normal")
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
resAsh <- lfcShrink(dds, coef=6, type="ashr")
## using 'ashr' for LFC shrinkage. If used in published research, please cite:
##     Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
##     https://doi.org/10.1093/biostatistics/kxw041
par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-20,20)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")

plotCounts(dds, gene=which.max(res$log2FoldChange), intgroup="Tissue")

plotCounts(dds, gene=which.min(res$log2FoldChange), intgroup="Tissue")

Transforming count data for visualization

vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds) # this gives log2(n + 1)

meanSdPlot(assay(vsd), main = "vsd")
## Warning in geom_hex(bins = bins, ...): Ignoring unknown parameters: `main`

meanSdPlot(assay(rld))

meanSdPlot(assay(ntd))

Will move forward with vst transformation for visualizations

Heatmap of count matrix

df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])

#view all genes
pheatmap(assay(vsd), cluster_rows=TRUE, show_rownames=FALSE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)

#view highest count genes
select <- order(rowMeans(counts(dds,normalized=TRUE)),
                decreasing=TRUE)[1:20]

pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)

#view most significantly differentially expressed genes

select <- order(res$padj)[1:20]

pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df)

Heatmap of the sample-to-sample distances

sampleDists <- dist(t(assay(vsd)))

sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$Tissue, vsd$Fragment, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)

Principal component plot of the samples

pcaData <- plotPCA(vsd, intgroup=c("Tissue", "Fragment"), returnData=TRUE)

percentVar <- round(100 * attr(pcaData, "percentVar"))
PCA <- ggplot(pcaData, aes(PC1, PC2, color=Tissue, shape=Fragment)) +
  geom_point(size=2) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

save_ggplot(PCA, "../output_RNA/differential_expression/PCA")

PCA_small <- ggplot(pcaData, aes(PC1, PC2, color=Tissue)) +
  geom_point(size=2) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed() + theme_bw()

ggsave(filename = paste0("../output_RNA/differential_expression/PCA_small", ".png"), plot = PCA_small, width = 4, height = 2.5, units = "in", dpi = 300)

Clearly, the majority of the variance in the data is explained by tissue type!

Annotation data

Download annotation files from genome website


# wget files
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt.gz

wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.EggNog_results.txt.gz

wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.KEGG_results.txt.gz

# move to references direcotry
mv *gz ../references

# unzip files
gunzip ../references/*gz
EggNog <- read.delim("../references/Pocillopora_acuta_HIv2.genes.EggNog_results.txt") %>% dplyr::rename("query" = X.query)

CDSearch <- read.delim("../references/Pocillopora_acuta_HIv2.genes.Conserved_Domain_Search_results.txt", quote = "") %>% dplyr::rename("query" = X.Query)

KEGG <- read.delim("../references/Pocillopora_acuta_HIv2.genes.KEGG_results.txt", header = FALSE) %>% dplyr::rename("query" = V1, "KeggTerm" = V2)
DE_05$query <- rownames(DE_05)
DE_05_annot <- DE_05 %>% left_join(CDSearch) %>% select(query,everything())
## Joining with `by = join_by(query)`
DE_05_eggnog <- DE_05 %>% left_join(EggNog) %>% select(query,everything())
## Joining with `by = join_by(query)`
annot_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(CDSearch)
## Joining with `by = join_by(query)`
eggnog_all <- as.data.frame(rownames(dds)) %>% dplyr::rename("query" = `rownames(dds)`) %>% left_join(EggNog)
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_eggnog), 
          file="../output_RNA/differential_expression/DE_05_eggnog_annotation.csv")
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])
gene_labels <- eggnog_all %>% select(query,PFAMs) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) %>% #replace NAs with "" for labelling purposes
  separate(PFAMs, into = c("PFAMs", "rest of name"), sep = ",(?=.*?,)", extra = "merge")
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 12723 rows [1, 2, 3, 4,
## 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 19, 20, 21, 23, 24, 25, ...].
#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200),cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[select,"PFAMs"], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi")

Genes of Interest

MarkerGenes <- read.csv("../references/Pacuta_MarkerGenes_Levy2021.csv") %>% dplyr::rename("query" = 1, "List" = 2, "definition" = 3)

MarkerGenes$def_short <- ifelse(nchar(MarkerGenes$definition) > 20, 
                            paste0(substr(MarkerGenes$definition, 1, 17), "..."), 
                            MarkerGenes$definition)
Biomin <- read.csv("../references/Pacuta_Biomin.csv") %>% dplyr::rename("query" = Pocillopora_acuta_best_hit) %>% select(-c(accessionnumber.geneID, Ref))

Biomin <- Biomin %>%
  group_by(query,List) %>%
  summarize(definition = paste(unique(definition), collapse = ","))
## `summarise()` has grouped output by 'query'. You can override using the
## `.groups` argument.
Biomin$def_short <- ifelse(nchar(Biomin$definition) > 40, 
                            paste0(substr(Biomin$definition, 1, 37), "..."), 
                            Biomin$definition)
DE_05$query <- rownames(DE_05)
DE_05_biomin <- DE_05 %>% left_join(Biomin) %>% select(query,everything()) %>% drop_na()
## Joining with `by = join_by(query)`
DE_05_marker <- DE_05 %>% left_join(MarkerGenes) %>% select(query,everything())  %>% drop_na()
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_marker), 
          file="../output_RNA/differential_expression/DE_05_markergene_annotation.csv")

biomin_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(Biomin) 
## Joining with `by = join_by(query)`
biomin_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(Biomin) 
## Joining with `by = join_by(query)`
markers_all_counts <- as.data.frame(counts(dds)) %>% mutate(query = rownames(dds)) %>% select(query,everything()) %>% left_join(MarkerGenes) 
## Joining with `by = join_by(query)`
markers_all_res <- as.data.frame(resLFC) %>% mutate(query = rownames(resLFC)) %>% select(query,everything()) %>% left_join(MarkerGenes) 
## Joining with `by = join_by(query)`
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])

#view biomin genes that are differntially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_biomin$query, ]), center = TRUE, scale = TRUE))
DE_biomin <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row = DE_05_biomin$def_short, fontsize_row = 5)
DE_biomin
save_ggplot(DE_biomin, "../output_RNA/differential_expression/DE_biomin")

#view marker genes that are differntially expressed

z_scores <- t(scale(t(assay(vsd)[DE_05_marker$query, ]), center = TRUE, scale = TRUE))
DE_marker <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=TRUE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,cutree_rows = 5,annotation_col=df,
         labels_row = DE_05_marker$List, fontsize_row = 4)
DE_marker
save_ggplot(DE_marker, "../output_RNA/differential_expression/DE_marker")

DE_05_marker_grouped <- DE_05_marker %>% arrange(List) %>% mutate(List = as.factor(List))

z_scores <- t(scale(t(assay(vsd)[DE_05_marker_grouped$quer, ]), center = TRUE, scale = TRUE))
DE_05_marker_grouped_plot <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row = DE_05_marker_grouped$List, fontsize_row = 5)

DE_05_marker_grouped_plot
save_ggplot(DE_05_marker_grouped_plot, "../output_RNA/differential_expression/DE_05_marker_grouped")

Visualizing Differential Expression

Biomin_volcano <- EnhancedVolcano(biomin_all_res, 
    lab = biomin_all_res$def_short,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 40)

save_ggplot(Biomin_volcano, "../output_RNA/differential_expression/Biomin_volcano")

Marker_volcano <- EnhancedVolcano(markers_all_res, 
    lab = markers_all_res$List,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(Marker_volcano, "../output_RNA/differential_expression/Marker_volcano")
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

Marker_volcano_names <- EnhancedVolcano(markers_all_res, 
    lab = markers_all_res$def_short,
    x = 'log2FoldChange',
    y = 'padj',
    pCutoff = 0.01,
    drawConnectors = TRUE,
    widthConnectors = 0.75,
    pointSize = 1,
    labSize = 2,boxedLabels = TRUE,max.overlaps = 60)

save_ggplot(Marker_volcano_names, "../output_RNA/differential_expression/Marker_volcano_names")

Trying to annotate un-annotated DE genes:

# wget protein sequence reference file
wget http://cyanophora.rutgers.edu/Pocillopora_acuta/Pocillopora_acuta_HIv2.genes.pep.faa.gz

# move to references direcotry
mv *gz ../references

# unzip files
gunzip ../references/*gz

#get the names of all the DEGs from the first column of the DEG csv file
tail -n +2 ../output_RNA/differential_expression/DEG_05.csv | cut -d',' -f1 | tr -d '"' > ../output_RNA/differential_expression/DEG_05_names.csv

#grep this file against the protein fasta file, first with wc -l to make sure the number of lines is correct (should be your number of DEGs)
grep -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa | wc -l

#grep each header with the protein sequence after ("-A 1") and save to a new file
grep  -A 1 -f ../output_RNA/differential_expression/DEG_05_names.csv ../references/Pocillopora_acuta_HIv2.genes.pep.faa > ../output_RNA/differential_expression/DEG_05_seqs.txt

nr database BLAST of DE_05 genes only

On andromeda:

Blastp-ing only the DE genes against the entire nr database (will take a while)

cd ../scripts
nano DEG_05_blast.sh
#!/bin/bash
#SBATCH --job-name="DE_blast"
#SBATCH -t 240:00:00
#SBATCH --export=NONE
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH --mail-user=zdellaert@uri.edu #your email to send notifications
#SBATCH --mem=500GB
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --account=putnamlab
#SBATCH --nodes=2 --ntasks-per-node=24

module load BLAST+/2.15.0-gompi-2023a

cd ../output_RNA/differential_expression #set working directory
mkdir blast
cd blast

#nr database location andromeda: /data/shared/ncbi-db/.ncbirc 
# points to current location: cat /data/shared/ncbi-db/.ncbirc
# [BLAST]
# BLASTDB=/data/shared/ncbi-db/2024-11-10

blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results.txt -outfmt 0 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 10

blastp -query ../DEG_05_seqs.txt -db nr -out DEG_05_blast_results_tab.txt -outfmt 6 -evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1

echo "Blast complete!" $(date)
sbatch DEG_05_blast.sh
cd ../output_RNA/differential_expression/blast

wc -l DEG_05_blast_results_tab.txt #3537 of the 3606 genes were annotated

#get just the NCBI database accession numbers for the blast results
cut -f2 DEG_05_blast_results_tab.txt > DEG_05_blast_accessions.txt

#remove any duplicates
sort -u  DEG_05_blast_accessions.txt > unique_DEG_05_blast_accessions.txt

wc -l unique_DEG_05_blast_accessions.txt #3404 of the 3537 annotations were unique

while read acc; do
  curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&id=$acc&rettype=gp&retmode=text" \
  | grep "DEFINITION" | sed 's/DEFINITION  //g' | awk -v id="$acc" '{print id "\t" $0}'
done < unique_DEG_05_blast_accessions.txt > DEG_05_blast_names.txt

wc -l DEG_05_blast_names.txt #3396 ; unsure why 8 are missing.

join -1 2 -2 1 -t $'\t' <(sort -k2 DEG_05_blast_results_tab.txt) <(sort DEG_05_blast_names.txt) > annotated_DEG_05_blast_results_tab.txt

SwissProt of ALL genes

On unity:

swissprot based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd

Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/

mkdir ../references/blast_dbs
cd ../references/blast_dbs
curl -O https://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.fasta.gz
mv uniprot_sprot.fasta.gz uniprot_sprot_r2024_10_02.fasta.gz
gunzip -k uniprot_sprot_r2024_10_02.fasta.gz
rm uniprot_sprot_r2024_10_02.fasta.gz

head uniprot_sprot_r2024_10_02.fasta
echo "Number of Sequences"
grep -c ">" uniprot_sprot_r2024_10_02.fasta
# 572214 sequences
module load blast-plus/2.14.1

makeblastdb \
-in ../references/blast_dbs/uniprot_sprot_r2024_10_02.fasta \
-dbtype prot \
-out ../references/blast_dbs/uniprot_sprot_r2024_10_02
cd ../scripts
nano blastp_SwissProt.sh
#!/bin/bash
#SBATCH -t 18:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=48
#SBATCH --mem=500GB
#SBATCH --export=NONE
#SBATCH --error=../scripts/outs_errs/"%x_error.%j" #write out slurm error reports
#SBATCH --output=../scripts/outs_errs/"%x_output.%j" #write out any program outpus
#SBATCH --mail-type=BEGIN,END,FAIL #email you when job starts, stops and/or fails
#SBATCH -D /project/pi_hputnam_uri_edu/zdellaert/LaserCoral #set working directory

module load blast-plus/2.14.1

cd references/
mkdir annotation

fasta="Pocillopora_acuta_HIv2.genes.pep.faa"

blastp \
-query $fasta \
-db blast_dbs/uniprot_sprot_r2024_10_02 \
-out annotation/blastp_SwissProt_out.tab \
-evalue 1E-05 \
-num_threads 48 \
-max_target_seqs 1 \
-max_hsps 1 \
-outfmt 6

echo "Blast complete!" $(date)
cd references/annotation/

tr '|' '\t' <  blastp_SwissProt_out.tab >  blastp_SwissProt_out_sep.tab
cd ../references/annotation/

curl -H "Accept: text/plain; format=tsv" "https://rest.uniprot.org/uniprotkb/stream?fields=accession%2Creviewed%2Cid%2Cprotein_name%2Cgene_names%2Corganism_name%2Clength%2Cgo_p%2Cgo%2Cgo_id%2Cgo_c%2Cgo_f&format=tsv&query=%28reviewed%3Atrue%29" -o SwissProt-Annot-GO_111524.tsv

wc -l SwissProt-Annot-GO_111524.tsv
#572215

All code below based on https://github.com/urol-e5/deep-dive/blob/main/D-Apul/code/20-Apul-gene-annotation.Rmd and https://github.com/urol-e5/deep-dive/blob/main/F-Pmea/code/20-Pmea-gene-annotation.Rmd

Steven’s notebook post here: https://sr320.github.io/tumbling-oysters/posts/sr320-27-go/

bltabl <- read.csv("../references/annotation/blastp_SwissProt_out_sep.tab", sep = '\t', header = FALSE)

spgo <- read.csv("../references/annotation/SwissProt-Annot-GO_111524.tsv", sep = '\t', header = TRUE)
annot_tab <- left_join(bltabl, spgo, by = c("V3" = "Entry")) %>%
  select(
    query = V1,
    blast_hit = V3,
    evalue = V13,
    ProteinNames = Protein.names,
    BiologicalProcess = Gene.Ontology..biological.process.,
    GeneOntologyIDs = Gene.Ontology.IDs
  )

head(annot_tab)
##                                        query blast_hit    evalue
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a    Q4JAI4  1.02e-37
## 2  Pocillopora_acuta_HIv2___RNAseq.g18333.t1    O08807 9.62e-116
## 3   Pocillopora_acuta_HIv2___RNAseq.g7985.t1    O74212 3.56e-158
## 4      Pocillopora_acuta_HIv2___TS.g15308.t1    Q09575  1.08e-12
## 5   Pocillopora_acuta_HIv2___RNAseq.g2057.t1    P0C1P0  8.81e-14
## 6   Pocillopora_acuta_HIv2___RNAseq.g4696.t1    Q9W2Q5  8.98e-69
##                                                                                                                                                                                                     ProteinNames
## 1                                                                                                                                              Methionine synthase (EC 2.1.1.-) (Homocysteine methyltransferase)
## 2 Peroxiredoxin-4 (EC 1.11.1.24) (Antioxidant enzyme AOE372) (Peroxiredoxin IV) (Prx-IV) (Thioredoxin peroxidase AO372) (Thioredoxin-dependent peroxide reductase A0372) (Thioredoxin-dependent peroxiredoxin 4)
## 3                                                                                                   Acyl-lipid (8-3)-desaturase (EC 1.14.19.30) (Delta(5) fatty acid desaturase) (Delta-5 fatty acid desaturase)
## 4                                                                                                                                                                                Uncharacterized protein K02A2.6
## 5                                                                              Phosphatidylinositol N-acetylglucosaminyltransferase subunit Y (Phosphatidylinositol-glycan biosynthesis class Y protein) (PIG-Y)
## 6                                                                                                                                                                   Calcium and integrin-binding family member 2
##                                                                                                                                                                                                                                                                                                                                                                                                                   BiologicalProcess
## 1                                                                                                                                                                                                                                                                                                                                                            methionine biosynthetic process [GO:0009086]; methylation [GO:0032259]
## 2 cell redox homeostasis [GO:0045454]; extracellular matrix organization [GO:0030198]; hydrogen peroxide catabolic process [GO:0042744]; male gonad development [GO:0008584]; negative regulation of male germ cell proliferation [GO:2000255]; protein maturation by protein folding [GO:0022417]; reactive oxygen species metabolic process [GO:0072593]; response to oxidative stress [GO:0006979]; spermatogenesis [GO:0007283]
## 3                                                                                                                                                                                                                                                                                                                 long-chain fatty acid biosynthetic process [GO:0042759]; unsaturated fatty acid biosynthetic process [GO:0006636]
## 4                                                                                                                                                                                                                                                                                                                                                                                                      DNA integration [GO:0015074]
## 5                                                                                                                                                                                                                                                                                                                                                                                      GPI anchor biosynthetic process [GO:0006506]
## 6                                                                                                                                                                                                                                                                                                                                                              calcium ion homeostasis [GO:0055074]; phototransduction [GO:0007602]
##                                                                                                                                                                                                          GeneOntologyIDs
## 1                                                                                                                                                                         GO:0003871; GO:0008270; GO:0009086; GO:0032259
## 2 GO:0005615; GO:0005737; GO:0005739; GO:0005783; GO:0005790; GO:0005829; GO:0006979; GO:0007283; GO:0008379; GO:0008584; GO:0022417; GO:0030198; GO:0042744; GO:0042802; GO:0045454; GO:0072593; GO:0140313; GO:2000255
## 3                                                                                                                                                 GO:0006636; GO:0016020; GO:0020037; GO:0042759; GO:0046872; GO:0102866
## 4                                                                                                                                                 GO:0003676; GO:0005737; GO:0008270; GO:0015074; GO:0019899; GO:0042575
## 5                                                                                                                                                                                                 GO:0000506; GO:0006506
## 6                                                                                                                                                             GO:0000287; GO:0005509; GO:0005737; GO:0007602; GO:0055074
write.table(annot_tab, 
            file = "../references/annotation/protein-GO.tsv", 
            sep = "\t", 
            row.names = FALSE, 
            quote = FALSE)

DE_05_SwissProt <- DE_05 %>% left_join(annot_tab) %>% select(query,everything()) 
## Joining with `by = join_by(query)`
write.csv(as.data.frame(DE_05_SwissProt), 
          file="../output_RNA/differential_expression/DE_05_SwissProt_annotation.csv")
df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])

DE_05_SwissProt$short_name <- ifelse(nchar(DE_05_SwissProt$ProteinNames) > 30, 
                            paste0(substr(DE_05_SwissProt$ProteinNames, 1, 27), "..."), 
                            DE_05_SwissProt$ProteinNames)

gene_labels <- DE_05_SwissProt %>% 
  select(query,short_name) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes

#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]
rownames(res)[select]
##  [1] "Pocillopora_acuta_HIv2___RNAseq.g6351.t1"  
##  [2] "Pocillopora_acuta_HIv2___RNAseq.g28575.t1a"
##  [3] "Pocillopora_acuta_HIv2___RNAseq.g27038.t1" 
##  [4] "Pocillopora_acuta_HIv2___RNAseq.g2165.t1"  
##  [5] "Pocillopora_acuta_HIv2___TS.g18104.t1"     
##  [6] "Pocillopora_acuta_HIv2___RNAseq.g14330.t3" 
##  [7] "Pocillopora_acuta_HIv2___RNAseq.g14025.t1" 
##  [8] "Pocillopora_acuta_HIv2___RNAseq.g14090.t1" 
##  [9] "Pocillopora_acuta_HIv2___RNAseq.g10378.t1" 
## [10] "Pocillopora_acuta_HIv2___RNAseq.g3832.t1"  
## [11] "Pocillopora_acuta_HIv2___RNAseq.g8006.t1"  
## [12] "Pocillopora_acuta_HIv2___RNAseq.g14253.t1" 
## [13] "Pocillopora_acuta_HIv2___RNAseq.g25431.t1" 
## [14] "Pocillopora_acuta_HIv2___RNAseq.11056_t"   
## [15] "Pocillopora_acuta_HIv2___RNAseq.g9588.t1"  
## [16] "Pocillopora_acuta_HIv2___TS.g30765.t1"     
## [17] "Pocillopora_acuta_HIv2___RNAseq.g14021.t1" 
## [18] "Pocillopora_acuta_HIv2___RNAseq.g19082.t1" 
## [19] "Pocillopora_acuta_HIv2___RNAseq.g16202.t1" 
## [20] "Pocillopora_acuta_HIv2___TS.g19991.t2"     
## [21] "Pocillopora_acuta_HIv2___RNAseq.g20860.t1" 
## [22] "Pocillopora_acuta_HIv2___RNAseq.g8588.t1"  
## [23] "Pocillopora_acuta_HIv2___RNAseq.g26604.t1" 
## [24] "Pocillopora_acuta_HIv2___TS.g18100.t1"     
## [25] "Pocillopora_acuta_HIv2___TS.g27642.t1b"    
## [26] "Pocillopora_acuta_HIv2___RNAseq.g11588.t1" 
## [27] "Pocillopora_acuta_HIv2___RNAseq.g25327.t1" 
## [28] "Pocillopora_acuta_HIv2___RNAseq.g12281.t1" 
## [29] "Pocillopora_acuta_HIv2___RNAseq.g19085.t1" 
## [30] "Pocillopora_acuta_HIv2___RNAseq.g19284.t1" 
## [31] "Pocillopora_acuta_HIv2___TS.g11360.t1"     
## [32] "Pocillopora_acuta_HIv2___RNAseq.g7803.t1"  
## [33] "Pocillopora_acuta_HIv2___TS.g16008.t2"     
## [34] "Pocillopora_acuta_HIv2___RNAseq.g22978.t3b"
## [35] "Pocillopora_acuta_HIv2___RNAseq.g2406.t1"  
## [36] "Pocillopora_acuta_HIv2___RNAseq.g8119.t1"  
## [37] "Pocillopora_acuta_HIv2___TS.g9414.t1"      
## [38] "Pocillopora_acuta_HIv2___RNAseq.g21374.t1" 
## [39] "Pocillopora_acuta_HIv2___RNAseq.g21000.t1" 
## [40] "Pocillopora_acuta_HIv2___RNAseq.g5252.t1"  
## [41] "Pocillopora_acuta_HIv2___RNAseq.g22853.t1" 
## [42] "Pocillopora_acuta_HIv2___RNAseq.g17117.t1" 
## [43] "Pocillopora_acuta_HIv2___RNAseq.g25759.t1" 
## [44] "Pocillopora_acuta_HIv2___TS.g16802.t1"     
## [45] "Pocillopora_acuta_HIv2___RNAseq.g24120.t1" 
## [46] "Pocillopora_acuta_HIv2___RNAseq.30415_t"   
## [47] "Pocillopora_acuta_HIv2___RNAseq.g14336.t1" 
## [48] "Pocillopora_acuta_HIv2___RNAseq.g26594.t2" 
## [49] "Pocillopora_acuta_HIv2___RNAseq.g19115.t1" 
## [50] "Pocillopora_acuta_HIv2___TS.g4983.t1"
z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 6)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_SwissProt")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row = gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_SwissProt")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_SwissProt")

df <- as.data.frame(colData(dds)[,c("Tissue","Fragment")])

DE_05_SwissProt$short_GO <- ifelse(nchar(DE_05_SwissProt$BiologicalProcess) > 30, 
                            paste0(substr(DE_05_SwissProt$BiologicalProcess, 1, 27), "..."), 
                            DE_05_SwissProt$BiologicalProcess)

gene_labels <- DE_05_SwissProt %>% select(query,short_GO) %>%
  mutate_all(~ ifelse(is.na(.), "", .)) #replace NAs with "" for labelling purposes

#view most significantly differentially expressed genes

select <- order(res$padj)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
top50_DE <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
top50_DE
save_ggplot(top50_DE, "../output_RNA/differential_expression/top50_DE_Blast2GO")

#view genes Higher in Aboral, Lower in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange,decreasing = TRUE)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_Aboral <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_Aboral
save_ggplot(up_Aboral, "../output_RNA/differential_expression/up_Aboral_Blast2GO")

#view genes Lower in Aboral, Higher in OralEpi, ordered by log2FoldChange
select <- order(res$log2FoldChange)[1:50]

z_scores <- t(scale(t(assay(vsd)[select, ]), center = TRUE, scale = TRUE))
up_OralEpi <- pheatmap(z_scores, color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), cluster_rows=FALSE, show_rownames=TRUE,
         cluster_cols=TRUE, cutree_cols = 2,annotation_col=df,
         labels_row =gene_labels[match(rownames(res)[select],(gene_labels$query)),2], fontsize_row = 5)
up_OralEpi
save_ggplot(up_OralEpi, "../output_RNA/differential_expression/up_OralEpi_Blast2GO")

## Updating Renv environment:

After you’ve confirmed your code works as expected, use renv::snapshot() to record the packages and their sources in the lockfile.

renv::snapshot()